This repository aims to provide an introduction series to reinforcement learning (RL) by delivering a walkthough on how to code different RL techniques.
A quick background review of RL is available here.
- Tutorial 1: Q-learning
- Tutorial 2: SARSA
- Tutorial 3: Exploring OpenAI gym
- Tutorial 4: Q-learning in OpenAI gym
- Tutorial 5: Deep Q-learning (DQN)
- Tutorial 6: Deep Convolutional Q-learning
- Tutorial 7: Reinforcement Learning with ROS and Gazebo
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Tutorial 8: Reinforcement Learning in DOOM(unfinished) - Tutorial 9: Deep Deterministic Policy Gradients (DDPG)
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Tutorial 10: Guided Policy Search (GPS)(unfinished) - Tutorial 11: A review of different AI techniques for RL (WIP)
- Tutorial 12: Reviewing Policy Gradient methods
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Tutorial 13: Continuous-state spaces with DQN(merged) - Tutorial 14: Benchmarking RL techniques
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Tutorial 15: Reviewing Vanilla Policy Gradient (VPG)(failed miserably)
- Chris Watkins, Learning from Delayed Rewards, Cambridge, 1989 (thesis)
- Awesome Reinforcement Learning repository, https://github.com/aikorea/awesome-rl
- Reinforcement learning CS9417ML, School of Computer Science & Engineering, UNSW Sydney, http://www.cse.unsw.edu.au/~cs9417ml/RL1/index.html
- Reinforcement learning blog posts, https://studywolf.wordpress.com/2012/11/25/reinforcement-learning-q-learning-and-exploration/
- OpenAI gym docs, https://gym.openai.com/docs
- Vincent Bons implementations, https://gist.github.com/wingedsheep
- David Silver's Deep Reinforcement Learning talk, http://videolectures.net/rldm2015_silver_reinforcement_learning/
- Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., & Zaremba, W. (2016). OpenAI Gym. arXiv preprint arXiv:1606.01540.
- https://sites.google.com/view/deep-rl-bootcamp/lectures
- https://github.com/vmayoral/gym-cryptocurrencies
